# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp from copy import deepcopy import mmengine from mmengine.config import Config, ConfigDict, DictAction from mmengine.evaluator import DumpResults from mmengine.registry import RUNNERS from mmengine.runner import Runner def parse_args(): parser = argparse.ArgumentParser( description='MMPreTrain test (and eval) a model') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') parser.add_argument( '--work-dir', help='the directory to save the file containing evaluation metrics') parser.add_argument('--out', help='the file to output results.') parser.add_argument( '--out-item', choices=['metrics', 'pred'], help='To output whether metrics or predictions. ' 'Defaults to output predictions.') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') parser.add_argument( '--amp', action='store_true', help='enable automatic-mixed-precision test') parser.add_argument( '--show-dir', help='directory where the visualization images will be saved.') parser.add_argument( '--show', action='store_true', help='whether to display the prediction results in a window.') parser.add_argument( '--interval', type=int, default=1, help='visualize per interval samples.') parser.add_argument( '--wait-time', type=float, default=2, help='display time of every window. (second)') parser.add_argument( '--no-pin-memory', action='store_true', help='whether to disable the pin_memory option in dataloaders.') parser.add_argument( '--tta', action='store_true', help='Whether to enable the Test-Time-Aug (TTA). If the config file ' 'has `tta_pipeline` and `tta_model` fields, use them to determine the ' 'TTA transforms and how to merge the TTA results. Otherwise, use flip ' 'TTA by averaging classification score.') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') # When using PyTorch version >= 2.0.0, the `torch.distributed.launch` # will pass the `--local-rank` parameter to `tools/train.py` instead # of `--local_rank`. parser.add_argument('--local_rank', '--local-rank', type=int, default=0) args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) return args def merge_args(cfg, args): """Merge CLI arguments to config.""" cfg.launcher = args.launcher # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) cfg.load_from = args.checkpoint # enable automatic-mixed-precision test if args.amp: cfg.test_cfg.fp16 = True # -------------------- visualization -------------------- if args.show or (args.show_dir is not None): assert 'visualization' in cfg.default_hooks, \ 'VisualizationHook is not set in the `default_hooks` field of ' \ 'config. Please set `visualization=dict(type="VisualizationHook")`' cfg.default_hooks.visualization.enable = True cfg.default_hooks.visualization.show = args.show cfg.default_hooks.visualization.wait_time = args.wait_time cfg.default_hooks.visualization.out_dir = args.show_dir cfg.default_hooks.visualization.interval = args.interval # -------------------- TTA related args -------------------- if args.tta: if 'tta_model' not in cfg: cfg.tta_model = dict(type='mmpretrain.AverageClsScoreTTA') if 'tta_pipeline' not in cfg: test_pipeline = cfg.test_dataloader.dataset.pipeline cfg.tta_pipeline = deepcopy(test_pipeline) flip_tta = dict( type='TestTimeAug', transforms=[ [ dict(type='RandomFlip', prob=1.), dict(type='RandomFlip', prob=0.) ], [test_pipeline[-1]], ]) cfg.tta_pipeline[-1] = flip_tta cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model) cfg.test_dataloader.dataset.pipeline = cfg.tta_pipeline # ----------------- Default dataloader args ----------------- default_dataloader_cfg = ConfigDict( pin_memory=True, collate_fn=dict(type='default_collate'), ) def set_default_dataloader_cfg(cfg, field): if cfg.get(field, None) is None: return dataloader_cfg = deepcopy(default_dataloader_cfg) dataloader_cfg.update(cfg[field]) cfg[field] = dataloader_cfg if args.no_pin_memory: cfg[field]['pin_memory'] = False set_default_dataloader_cfg(cfg, 'test_dataloader') if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) return cfg def main(): args = parse_args() if args.out is None and args.out_item is not None: raise ValueError('Please use `--out` argument to specify the ' 'path of the output file before using `--out-item`.') # load config cfg = Config.fromfile(args.config) # merge cli arguments to config cfg = merge_args(cfg, args) # build the runner from config if 'runner_type' not in cfg: # build the default runner runner = Runner.from_cfg(cfg) else: # build customized runner from the registry # if 'runner_type' is set in the cfg runner = RUNNERS.build(cfg) if args.out and args.out_item in ['pred', None]: runner.test_evaluator.metrics.append( DumpResults(out_file_path=args.out)) # start testing metrics = runner.test() if args.out and args.out_item == 'metrics': mmengine.dump(metrics, args.out) if __name__ == '__main__': main()